Computational Data Science
Learning outcomes
On successful completion of the Computational Data Science major students will be able to:
| No. | Learning outcomes |
|---|---|
| 1 | Develop a broad and coherent body of knowledge in computational data science, describing the relationships between context-specific knowledge and data and evaluating how these can guide data analytics. |
| 2 | Develop deep knowledge of the underlying concepts and principles of experimental design, analysis and data outputs, of the relationships between these concepts, and of potential pitfalls. |
| 3 | Use quantitative models or visualisation methods on multiple types of data. |
| 4 | Identify data analytical approaches appropriate to a specific problem in data analysis, simulation-based modelling or equation-based modelling. |
| 5 | Manage data, metadata and derived knowledge, using appropriate storage, access and administration tools. |
| 6 | Communicate concepts and findings in computational data science through a range of modes for a variety of purposes and audiences, using evidence-based arguments that are robust to critique. |
| 7 | Identify data analytical approaches appropriate to a specific problem in data analysis, simulation-based modelling or equation-based modelling. |
| 8 | Create and use databases and graphical information systems using programming skills. |
| 9 | Address authentic problems in computational data science, working professionally and ethically and with consideration of cross-cultural perspectives, within collaborative, interdisciplinary teams. |
